{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的各种包\n",
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 打开数据文档\n",
    "df0,metadata =pyreadstat.read_sav(R\"C:\\Users\\yh1125\\Desktop\\xuan\\企业形象调查原始数据.sav\",apply_value_formats=True)\n",
    "# 使用自定义工具包中的函数读取spss格式文件\n",
    "df1,metadata = mytools.read_spss(R\"C:\\Users\\yh1125\\Desktop\\xuan\\企业形象调查原始数据.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 清理数据\n",
    "## 清理空白值\n",
    "### 查看所有空白值\n",
    "temp = df1[df1.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 删除指定的空白值\n",
    "\"\"\"\n",
    "## 使用dropna方法删除空值 #card\n",
    "\n",
    "1. `axis`，用于指定操作行还是列。`0`代表行，`1`代表列，默认为`0`。\n",
    "2. `how`，其值为`\"any\"`和`\"all\"`，`\"any\"`表示只要有空值，就删除。`\"all\"`表示一行或一列的所有值为空才删除。默认为`\"any\"`。\n",
    "3. `thresh`，表示保留至少含有`n`个非`na`数值的行或列。默认为`None`。\n",
    "4. `subset`，用来指定在那些列中查找缺失值。例如`df.dropna(subset=['name', 'born'])`表示删除在`'name'`和` 'born'`列含有缺失值的行。默认为`None`。\n",
    "5. `inplace`，表示是否直接在原DataFrame修改。默认为`False`，即不修改原数据框。\n",
    "\"\"\"\n",
    "df2 = df1.dropna(thresh=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 如果要删除所有空值，执行下面语句即可\n",
    "df3 = df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 删除重复值\n",
    "df4 = df3.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
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       "促销活动       float64\n",
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      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查数据类型\n",
    "\n",
    "### 查看所有变量的数据类型\n",
    "df4.dtypes.to_frame()\n",
    "### 使用dtypes属性可以查看所有变量或指定变量的类型。\n",
    "### 使用to_frame方法的目的在于输出更加整洁的文本，非必须。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号         int32\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 使用astypes方法设定变量的类型\n",
    "df5 = df4.astype({'问卷编号':'int'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男性      211\n",
       "女性      204\n",
       "22.0      1\n",
       "Name: 性别, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "异常值清理\n",
    "\n",
    "类别变量异常值的查看及修改\n",
    "\n",
    "使用value_counts方法可以查看类别变量的取值及次数\n",
    "\"\"\"\n",
    "df5['性别'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\"\"\" 列出所有类别变量 \"\"\"\n",
    "for col in df5.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "### 使用自定义函数打印初全部类别变量取值\n",
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('性别==22')\n",
    "df5.loc[322,'性别'] = '男性'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 找出类别变量的异常值并修改\n",
    "df5.query('必胜客综合评价==0')\n",
    "df5.loc[353,'欢乐聚餐'] = '不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       416.000000\n",
       "mean        513.247596\n",
       "std        4893.048164\n",
       "min           1.000000\n",
       "25%         140.750000\n",
       "50%         259.000000\n",
       "75%         392.000000\n",
       "max      100000.000000\n",
       "Name: 问卷编号, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看数值变量的异常值\n",
    "### 使用describe方法对数值变量进行描述统计，可得到最小值、最大值、方差等信息\n",
    "df5['问卷编号'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       416.000000\n",
      "mean        513.247596\n",
      "std        4893.048164\n",
      "min           1.000000\n",
      "25%         140.750000\n",
      "50%         259.000000\n",
      "75%         392.000000\n",
      "max      100000.000000\n",
      "Name: 问卷编号, dtype: float64\n",
      "count    416.000000\n",
      "mean      24.127404\n",
      "std        6.501946\n",
      "min       10.000000\n",
      "25%       20.000000\n",
      "50%       22.000000\n",
      "75%       26.000000\n",
      "max       60.000000\n",
      "Name: 年龄, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.137019\n",
      "std        0.344282\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 快乐童年, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.293269\n",
      "std        0.455809\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 都市生活, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.170673\n",
      "std        0.598959\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max       10.000000\n",
      "Name: 洋溢青春, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.429669\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        4.000000\n",
      "Name: 其他感受, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.151442\n",
      "std        0.358911\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 健康安全, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.572115\n",
      "std        0.495368\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速便捷, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.314904\n",
      "std        0.465037\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 价格昂贵, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.165865\n",
      "std        0.372408\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 口感不好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.163462\n",
      "std        0.370231\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 品类太少, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.076923\n",
      "std        0.266790\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他评价, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.456731\n",
      "std        0.498724\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速好吃, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.394588\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 服务好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 种类多, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.168269\n",
      "std        0.374556\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 价格便宜, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.086538\n",
      "std        0.281496\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他吸引因素, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.319712\n",
      "std        0.466926\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 服务态度需改进, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 送餐速度, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.358173\n",
      "std        0.480041\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 就餐环境, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.391827\n",
      "std        0.488746\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品价格, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.237981\n",
      "std        0.426360\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 促销活动, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品种类, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.045673\n",
      "std        0.209026\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他改进因素, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "### 使用自定义函数对不同类型的数值变量进行描述统计\n",
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 逻辑一致性清理\n",
    "### 构造逻辑判断表达式\n",
    "c1 = '常去的快餐店 == \"德克士\" and 德克士类型感知 == \"普通大众\"'\n",
    "### 筛选出满足条件的数据\n",
    "temp = df5.query(c1)[['常去的快餐店','德克士类型感知']]\n",
    "### 删除满足上述条件的数据\n",
    "df6 = df5.drop(temp.index)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2 = '性别 == \"男性\" and 伙食费 == \"三百到六百\"'\n",
    "temp = df6.query(c2)[['性别','伙食费']]\n",
    "### 删除满足上述条件的数据\n",
    "df7 = df6.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据清理完毕\n",
    "df = df7.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述统计\n",
    "## 单变量描述统计\n",
    "### 无序类别变量（定类变量）描述统计，\n",
    "### 可使用频数频率表、众数、柱状图等方式进行描述\n",
    "result = df['伙食费'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>伙食费</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>六百到九百</td>\n",
       "      <td>138</td>\n",
       "      <td>35.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>九百到一千二</td>\n",
       "      <td>120</td>\n",
       "      <td>31.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>一千二以上</td>\n",
       "      <td>85</td>\n",
       "      <td>22.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>三百到六百</td>\n",
       "      <td>42</td>\n",
       "      <td>10.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>385</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      伙食费   个数    百分比\n",
       "0   六百到九百  138  35.84\n",
       "1  九百到一千二  120  31.17\n",
       "2   一千二以上   85  22.08\n",
       "3   三百到六百   42  10.91\n",
       "4      总和  385  100.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['伙食费'] = df['伙食费'].value_counts().index\n",
    "b['个数'] = df['伙食费'].value_counts().values\n",
    "b['百分比'] = df['伙食费'].value_counts(normalize=True).values * 100\n",
    "b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'伙食费':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "pd.concat([b,total_row],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>伙食费</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>六百到九百</td>\n",
       "      <td>138</td>\n",
       "      <td>35.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>九百到一千二</td>\n",
       "      <td>120</td>\n",
       "      <td>31.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>一千二以上</td>\n",
       "      <td>85</td>\n",
       "      <td>22.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>三百到六百</td>\n",
       "      <td>42</td>\n",
       "      <td>10.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>385</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      伙食费   个数    百分比\n",
       "0   六百到九百  138  35.84\n",
       "1  九百到一千二  120  31.17\n",
       "2   一千二以上   85  22.08\n",
       "3   三百到六百   42  10.91\n",
       "4      总和  385  100.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'伙食费')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女性</td>\n",
       "      <td>204</td>\n",
       "      <td>52.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男性</td>\n",
       "      <td>181</td>\n",
       "      <td>47.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>总和</td>\n",
       "      <td>385</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     性别   个数    百分比\n",
       "0    女性  204  52.99\n",
       "1    男性  181  47.01\n",
       "2  22.0    0    0.0\n",
       "3    总和  385  100.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'性别')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 调用外部自定义函数绘制柱状图\n",
    "mytools.show_bar(df,'伙食费')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比（%）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>132</td>\n",
       "      <td>35.87</td>\n",
       "      <td>35.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>55</td>\n",
       "      <td>14.95</td>\n",
       "      <td>50.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>德克士</td>\n",
       "      <td>73</td>\n",
       "      <td>19.84</td>\n",
       "      <td>70.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>43</td>\n",
       "      <td>11.68</td>\n",
       "      <td>82.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>其它</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>65</td>\n",
       "      <td>17.66</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>368</td>\n",
       "      <td>100.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比 累计百分比（%）\n",
       "0     肯德基  132  35.87    35.87\n",
       "1     麦当劳   55  14.95    50.82\n",
       "2     德克士   73  19.84    70.66\n",
       "3     必胜客   43  11.68    82.34\n",
       "4      其它    0    0.0    82.34\n",
       "5  从不去快餐店   65  17.66    100.0\n",
       "6      总和  368  100.0         "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 单变量描述统计\n",
    "### 有序类别变量（定序变量）描述统计，\n",
    "### 可使用频数频率表（累计频率）、众数、柱状图等方式进行描述\n",
    "from pandas.api.types import CategoricalDtype\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['肯德基','麦当劳', '德克士','必胜客', '其它', '从不去快餐店'], ordered=True)\n",
    "df = df.astype({'常去的快餐店':cat_dtype})\n",
    "mytools.ordinal_desc(df,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'常去的快餐店',sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    385.000000\n",
       "mean       4.935065\n",
       "std        2.173093\n",
       "min        0.000000\n",
       "25%        4.000000\n",
       "50%        5.000000\n",
       "75%        6.000000\n",
       "max       10.000000\n",
       "Name: 认知维度, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 单变量描述统计\n",
    "### 数值变量描述统计，\n",
    "### 可使用直方图、平均值、极差、四分位值等方式进行描述\n",
    "df['认知维度']= df['性别'].cat.codes + df['伙食费'].cat.codes + df['常去的快餐店'].cat.codes\n",
    "df['认知维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='认知维度', ylabel='Count'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 绘制直方图\n",
    "import seaborn as sns\n",
    "sns.histplot(data=df, x=\"认知维度\",binwidth=1,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述统计\n",
    "## 双变量描述统计\n",
    "### 定类与定类\n",
    "result = pd.crosstab(\n",
    "        df['性别'],\n",
    "        df['常去的快餐店'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>肯德基</th>\n",
       "      <th>麦当劳</th>\n",
       "      <th>德克士</th>\n",
       "      <th>必胜客</th>\n",
       "      <th>从不去快餐店</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女性</th>\n",
       "      <td>44.7</td>\n",
       "      <td>47.27</td>\n",
       "      <td>71.23</td>\n",
       "      <td>46.51</td>\n",
       "      <td>53.85</td>\n",
       "      <td>52.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男性</th>\n",
       "      <td>55.3</td>\n",
       "      <td>52.73</td>\n",
       "      <td>28.77</td>\n",
       "      <td>53.49</td>\n",
       "      <td>46.15</td>\n",
       "      <td>47.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "常去的快餐店   肯德基    麦当劳    德克士    必胜客  从不去快餐店     合计\n",
       "性别                                              \n",
       "女性      44.7  47.27  71.23  46.51   53.85  52.17\n",
       "男性      55.3  52.73  28.77  53.49   46.15  47.83"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为：0.040，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_y = mytools.goodmanKruska_tau_y(df, '常去的快餐店', '性别')\n",
    "f'tau_y值为：{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>性别</th>\n",
       "      <th>女性</th>\n",
       "      <th>男性</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三百到六百</th>\n",
       "      <td>20.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六百到九百</th>\n",
       "      <td>32.84</td>\n",
       "      <td>39.23</td>\n",
       "      <td>35.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九百到一千二</th>\n",
       "      <td>27.45</td>\n",
       "      <td>35.36</td>\n",
       "      <td>31.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一千二以上</th>\n",
       "      <td>19.12</td>\n",
       "      <td>25.41</td>\n",
       "      <td>22.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "性别         女性     男性     合计\n",
       "伙食费                        \n",
       "三百到六百   20.59   0.00  10.91\n",
       "六百到九百   32.84  39.23  35.84\n",
       "九百到一千二  27.45  35.36  31.17\n",
       "一千二以上   19.12  25.41  22.08"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 定序与定序\n",
    "### 一定要注意参与运算的数据必须为定序变量\n",
    "\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['三百到六百','六百到九百', '九百到一千二','一千二以上'], ordered=True)\n",
    "df = df.astype({'伙食费':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['性别'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22497562560935977"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "# 取得各个定序变量\n",
    "x = df['性别'].cat.codes\n",
    "y = df['伙食费'].cat.codes\n",
    "dy = stats.somersd(x, y)\n",
    "# 打印萨莫司dy的值\n",
    "dy.statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'萨莫司dy值为：0.225，该值属于弱相关，p值为0.000，拒绝虚无假设，接收研究假设。'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = dy.pvalue\n",
    "f\"萨莫司dy值为：{dy.statistic:.3f}，该值属于{mytools.draw_on_corr(dy.statistic)}，p值为{p:.3f}，{mytools.p_result(p)['conclusion']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 构造另一个定距变量\n",
    "df['情感维度']= df['德克士类型感知'].cat.codes + df['必胜客综合评价'].cat.codes + df['性别'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 定距与定距\n",
    "mytools.gen_scatter(df,'情感维度','认知维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'积矩相关系数r为：-0.043，决定系数r平方为：0.002，相关强度为极弱相关或不相关。'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.pearsonr(df['情感维度'], df['认知维度'])\n",
    "f\"积矩相关系数r为：{r:.3f}，决定系数r平方为：{r*r:.3f}，相关强度为{mytools.draw_on_r(r*r)}。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 定类与定距\n",
    "\n",
    "sns.boxplot(\n",
    "        x='性别',\n",
    "        y='认知维度',\n",
    "        data=df,\n",
    "        color='white',\n",
    "        linewidth=1,\n",
    "        width=0.5,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'statsmodels'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [76], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m# 计算相关比率\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mstatsmodels\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mformula\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mapi\u001b[39;00m \u001b[39mimport\u001b[39;00m ols\n\u001b[0;32m      3\u001b[0m model \u001b[39m=\u001b[39m ols(\u001b[39m'\u001b[39m\u001b[39m认知维度 ~ 必胜客综合评价\u001b[39m\u001b[39m'\u001b[39m, df)\u001b[39m.\u001b[39mfit()\n\u001b[0;32m      4\u001b[0m eta_2 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mrsquared\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'statsmodels'"
     ]
    }
   ],
   "source": [
    "# 计算相关比率\n",
    "from statsmodels.formula.api import ols\n",
    "model = ols('认知维度 ~ 必胜客综合评价', df).fit()\n",
    "eta_2 = model.rsquared\n",
    "f\"\"\"相关比率为：{eta_2:.3f}，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为{mytools.draw_on_eta2(eta_2)}。\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytools.to_sav(df_clean,metadata,'./data/indentity问卷数据数据清理后.sav')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8b205521816117bdb38399e3aec0161a840ddff3eae2c53c30f7b40fc43ea645"
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